Add docstring tests

doubleml/data/did_data.py

@@ -63,7 +63,7 @@ class DoubleMLDIDData(DoubleMLData):
     >>> from doubleml.did.datasets import make_did_SZ2020
     >>> # initialization from pandas.DataFrame
     >>> df = make_did_SZ2020(return_type='DataFrame')
-    >>> obj_dml_data_from_df = DoubleMLDIDData(df, 'y', 'd', 't')
+    >>> obj_dml_data_from_df = DoubleMLDIDData(df, 'y', 'd')
     >>> # initialization from np.ndarray
     >>> (x, y, d, t) = make_did_SZ2020(return_type='array')
     >>> obj_dml_data_from_array = DoubleMLDIDData.from_arrays(x, y, d, t=t)

doubleml/data/rdd_data.py

@@ -61,14 +61,17 @@ class DoubleMLRDDData(DoubleMLData):
 
     Examples
     --------
+    >>> import numpy as np
+    >>> import pandas as pd
     >>> from doubleml import DoubleMLRDDData
-    >>> from doubleml.rdd.datasets import make_rdd_data
+    >>> from doubleml.rdd.datasets import make_simple_rdd_data
     >>> # initialization from pandas.DataFrame
-    >>> df = make_rdd_data(return_type='DataFrame')
-    >>> obj_dml_data_from_df = DoubleMLRDDData(df, 'y', 'd', 's')
+    >>> data = make_simple_rdd_data(return_type='DataFrame')
+    >>> columns = ["y", "d", "score"] + ["x" + str(i) for i in range(data["X"].shape[1])]
+    >>> df = pd.DataFrame(np.column_stack((data["Y"], data["D"], data["score"], data["X"])), columns=columns)
+    >>> obj_dml_data_from_df = DoubleMLRDDData(df, 'y', 'd', score_col='s')
     >>> # initialization from np.ndarray
-    >>> (x, y, d, s) = make_rdd_data(return_type='array')
-    >>> obj_dml_data_from_array = DoubleMLRDDData.from_arrays(x, y, d, s=s)
+    >>> obj_dml_data_from_array = DoubleMLRDDData.from_arrays(data["X"], data["Y"], data["D"], score=data["score"])
     """
 
     def __init__(
@@ -160,10 +163,13 @@ def from_arrays(
 
         Examples
         --------
+        >>> import numpy as np
+        >>> import pandas as pd
         >>> from doubleml import DoubleMLRDDData
-        >>> from doubleml.rdd.datasets import make_rdd_data
-        >>> (x, y, d, s) = make_rdd_data(return_type='array')
-        >>> obj_dml_data_from_array = DoubleMLRDDData.from_arrays(x, y, d, s=s)
+        >>> from doubleml.rdd.datasets import make_simple_rdd_data
+        >>> # initialization from pandas.DataFrame
+        >>> data = make_simple_rdd_data(return_type='DataFrame')
+        >>> obj_dml_data_from_array = DoubleMLRDDData.from_arrays(data["X"], data["Y"], data["D"], score=data["score"])
         """
         # Prepare score variable
         score = check_array(score, ensure_2d=False, allow_nd=False)

doubleml/data/ssm_data.py

@@ -66,9 +66,9 @@ class DoubleMLSSMData(DoubleMLData):
     >>> from doubleml.irm.datasets import make_ssm_data
     >>> # initialization from pandas.DataFrame
     >>> df = make_ssm_data(return_type='DataFrame')
-    >>> obj_dml_data_from_df = DoubleMLSSMData(df, 'y', 'd', 's')
+    >>> obj_dml_data_from_df = DoubleMLSSMData(df, 'y', 'd', s_col='s')
     >>> # initialization from np.ndarray
-    >>> (x, y, d, s) = make_ssm_data(return_type='array')
+    >>> (x, y, d, _, s) = make_ssm_data(return_type='array')
     >>> obj_dml_data_from_array = DoubleMLSSMData.from_arrays(x, y, d, s=s)
     """
 
@@ -186,7 +186,7 @@ def from_arrays(
         --------
         >>> from doubleml import DoubleMLSSMData
         >>> from doubleml.irm.datasets import make_ssm_data
-        >>> (x, y, d, s) = make_ssm_data(return_type='array')
+        >>> (x, y, d, _, s) = make_ssm_data(return_type='array')
         >>> obj_dml_data_from_array = DoubleMLSSMData.from_arrays(x, y, d, s=s)
         """
         # Prepare selection variable

doubleml/did/did.py

@@ -75,8 +75,9 @@ class DoubleMLDID(LinearScoreMixin, DoubleML):
     >>> obj_dml_data = dml.DoubleMLDIDData(data, 'y', 'd')
     >>> dml_did_obj = dml.DoubleMLDID(obj_dml_data, ml_g, ml_m)
     >>> dml_did_obj.fit().summary
-           coef   std err         t     P>|t|     2.5 %   97.5 %
-    d -2.685104  1.798071 -1.493325  0.135352 -6.209257  0.83905
+           coef   std err         t     P>|t|     2.5 %    97.5 %
+    d -2.840718  1.760386 -1.613691  0.106595 -6.291011  0.609575
+
     """
 
     def __init__(

doubleml/did/did_cs.py

@@ -74,8 +74,8 @@ class DoubleMLDIDCS(LinearScoreMixin, DoubleML):
     >>> obj_dml_data = dml.DoubleMLDIDData(data, 'y', 'd', t_col='t')
     >>> dml_did_obj = dml.DoubleMLDIDCS(obj_dml_data, ml_g, ml_m)
     >>> dml_did_obj.fit().summary
-           coef   std err         t     P>|t|      2.5 %     97.5 %
-    d -6.604603  8.725802 -0.756905  0.449107 -23.706862  10.497655
+         coef   std err         t     P>|t|      2.5 %    97.5 %
+    d -4.9944  7.561785 -0.660479  0.508947 -19.815226  9.826426
     """
 
     def __init__(

doubleml/did/did_multi.py

@@ -134,7 +134,22 @@ class DoubleMLDIDMulti:
     ...     gt_combinations="standard",
     ...     control_group="never_treated",
     ... )
-    >>> print(dml_did_obj.fit())
+    >>> print(dml_did_obj.fit().summary)
+                                      coef   std err  ...     2.5 %    97.5 %
+    ATT(2025-03,2025-01,2025-02) -0.797617  0.459617  ... -1.698450  0.103215
+    ATT(2025-03,2025-02,2025-03)  0.270311  0.456453  ... -0.624320  1.164941
+    ATT(2025-03,2025-02,2025-04)  0.628213  0.895275  ... -1.126494  2.382919
+    ATT(2025-03,2025-02,2025-05)  1.281360  1.327121  ... -1.319750  3.882470
+    ATT(2025-04,2025-01,2025-02) -0.078095  0.407758  ... -0.877287  0.721097
+    ATT(2025-04,2025-02,2025-03)  0.223625  0.479288  ... -0.715764  1.163013
+    ATT(2025-04,2025-03,2025-04)  1.008674  0.455564  ...  0.115785  1.901563
+    ATT(2025-04,2025-03,2025-05)  2.941047  0.832991  ...  1.308415  4.573679
+    ATT(2025-05,2025-01,2025-02) -0.102282  0.454129  ... -0.992359  0.787795
+    ATT(2025-05,2025-02,2025-03)  0.108742  0.547794  ... -0.964914  1.182399
+    ATT(2025-05,2025-03,2025-04)  0.253610  0.422984  ... -0.575423  1.082643
+    ATT(2025-05,2025-04,2025-05)  1.264255  0.487934  ...  0.307923  2.220587
+    <BLANKLINE>
+    [12 rows x 6 columns]
     """
 
     def __init__(

doubleml/double_ml.py

@@ -1207,14 +1207,12 @@ def evaluate_learners(self, learners=None, metric=_rmse):
         >>> data = make_irm_data(theta=0.5, n_obs=500, dim_x=20, return_type='DataFrame')
         >>> obj_dml_data = dml.DoubleMLData(data, 'y', 'd')
         >>> dml_irm_obj = dml.DoubleMLIRM(obj_dml_data, ml_g, ml_m)
-        >>> dml_irm_obj.fit()
+        >>> _ = dml_irm_obj.fit()
         >>> def mae(y_true, y_pred):
-        >>>     subset = np.logical_not(np.isnan(y_true))
-        >>>     return mean_absolute_error(y_true[subset], y_pred[subset])
+        ...     subset = np.logical_not(np.isnan(y_true))
+        ...     return mean_absolute_error(y_true[subset], y_pred[subset])
         >>> dml_irm_obj.evaluate_learners(metric=mae)
-        {'ml_g0': array([[0.85974356]]),
-         'ml_g1': array([[0.85280376]]),
-         'ml_m': array([[0.35365143]])}
+        {'ml_g0': array([[0.88173585]]), 'ml_g1': array([[0.83854057]]), 'ml_m': array([[0.35871235]])}
         """
         # if no learners are provided try to evaluate all learners
         if learners is None:

doubleml/double_ml_sampling_mixins.py

@@ -91,19 +91,18 @@ def set_sample_splitting(self, all_smpls, all_smpls_cluster=None):
         >>> ml_m = learner
         >>> obj_dml_data = make_plr_CCDDHNR2018(n_obs=10, alpha=0.5)
         >>> dml_plr_obj = dml.DoubleMLPLR(obj_dml_data, ml_g, ml_m)
-        >>> # simple sample splitting with two folds and without cross-fitting
-        >>> smpls = ([0, 1, 2, 3, 4], [5, 6, 7, 8, 9])
-        >>> dml_plr_obj.set_sample_splitting(smpls)
         >>> # sample splitting with two folds and cross-fitting
         >>> smpls = [([0, 1, 2, 3, 4], [5, 6, 7, 8, 9]),
-        >>>          ([5, 6, 7, 8, 9], [0, 1, 2, 3, 4])]
-        >>> dml_plr_obj.set_sample_splitting(smpls)
+        ...          ([5, 6, 7, 8, 9], [0, 1, 2, 3, 4])]
+        >>> dml_plr_obj.set_sample_splitting(smpls) # doctest: +ELLIPSIS
+        <doubleml.plm.plr.DoubleMLPLR object at 0x...>
         >>> # sample splitting with two folds and repeated cross-fitting with n_rep = 2
         >>> smpls = [[([0, 1, 2, 3, 4], [5, 6, 7, 8, 9]),
-        >>>           ([5, 6, 7, 8, 9], [0, 1, 2, 3, 4])],
-        >>>          [([0, 2, 4, 6, 8], [1, 3, 5, 7, 9]),
-        >>>           ([1, 3, 5, 7, 9], [0, 2, 4, 6, 8])]]
-        >>> dml_plr_obj.set_sample_splitting(smpls)
+        ...           ([5, 6, 7, 8, 9], [0, 1, 2, 3, 4])],
+        ...          [([0, 2, 4, 6, 8], [1, 3, 5, 7, 9]),
+        ...           ([1, 3, 5, 7, 9], [0, 2, 4, 6, 8])]]
+        >>> dml_plr_obj.set_sample_splitting(smpls) # doctest: +ELLIPSIS
+        <doubleml.plm.plr.DoubleMLPLR object at 0x...>
         """
         self._smpls, self._smpls_cluster, self._n_rep, self._n_folds = _check_sample_splitting(
             all_smpls, all_smpls_cluster, self._dml_data, self._is_cluster_data, n_obs=self._n_obs_sample_splitting

doubleml/irm/cvar.py

@@ -91,8 +91,9 @@ class DoubleMLCVAR(LinearScoreMixin, DoubleML):
     >>> obj_dml_data = dml.DoubleMLData(data, 'y', 'd')
     >>> dml_cvar_obj = dml.DoubleMLCVAR(obj_dml_data, ml_g, ml_m, treatment=1, quantile=0.5)
     >>> dml_cvar_obj.fit().summary
-           coef   std err          t         P>|t|     2.5 %    97.5 %
-    d  1.591441  0.095781  16.615498  5.382582e-62  1.403715  1.779167
+           coef   std err         t         P>|t|     2.5 %    97.5 %
+    d  1.588364  0.096616  16.43989  9.909942e-61  1.398999  1.777728
+
     """
 
     def __init__(

doubleml/irm/iivm.py

@@ -91,7 +91,7 @@ class DoubleMLIIVM(LinearScoreMixin, DoubleML):
     >>> dml_iivm_obj = dml.DoubleMLIIVM(obj_dml_data, ml_g, ml_m, ml_r)
     >>> dml_iivm_obj.fit().summary
            coef   std err         t     P>|t|     2.5 %    97.5 %
-    d  0.378351  0.190648  1.984551  0.047194  0.004688  0.752015
+    d  0.362398  0.191578  1.891649  0.058538 -0.013088  0.737884
 
     Notes
     -----

doubleml/irm/irm.py

@@ -94,7 +94,7 @@ class DoubleMLIRM(LinearScoreMixin, DoubleML):
     >>> dml_irm_obj = dml.DoubleMLIRM(obj_dml_data, ml_g, ml_m)
     >>> dml_irm_obj.fit().summary
            coef   std err         t     P>|t|     2.5 %    97.5 %
-    d  0.414073  0.238529  1.735941  0.082574 -0.053436  0.881581
+    d  0.371972  0.206802  1.798685  0.072069 -0.033353  0.777297
 
     Notes
     -----

doubleml/irm/ssm.py

@@ -67,7 +67,7 @@ class DoubleMLSSM(LinearScoreMixin, DoubleML):
     >>> import numpy as np
     >>> import doubleml as dml
     >>> from doubleml import DoubleMLSSMData
-    >>> from sklearn.linear_model import LassoCV, LogisticRegressionCV()
+    >>> from sklearn.linear_model import LassoCV, LogisticRegressionCV
     >>> from sklearn.base import clone
     >>> np.random.seed(3146)
     >>> n = 2000
@@ -88,10 +88,10 @@ class DoubleMLSSM(LinearScoreMixin, DoubleML):
     >>> ml_g_sim = clone(learner)
     >>> ml_pi_sim = clone(learner_class)
     >>> ml_m_sim = clone(learner_class)
-    >>> obj_dml_sim = DoubleMLS(simul_data, ml_g_sim, ml_pi_sim, ml_m_sim)
+    >>> obj_dml_sim = DoubleMLSSM(simul_data, ml_g_sim, ml_pi_sim, ml_m_sim)
     >>> obj_dml_sim.fit().summary
-          coef   std err         t         P>|t|     2.5 %    97.5 %
-    d  0.49135  0.070534  6.966097  3.258541e-12  0.353105  0.629595
+           coef   std err         t         P>|t|    2.5 %    97.5 %
+    d  0.518517  0.065535  7.912033  2.532202e-15  0.39007  0.646963
 
     Notes
     -----

doubleml/plm/pliv.py

@@ -74,8 +74,8 @@ class DoubleMLPLIV(LinearScoreMixin, DoubleML):
     >>> obj_dml_data = dml.DoubleMLData(data, 'y', 'd', z_cols='Z1')
     >>> dml_pliv_obj = dml.DoubleMLPLIV(obj_dml_data, ml_l, ml_m, ml_r)
     >>> dml_pliv_obj.fit().summary
-           coef   std err         t         P>|t|     2.5 %    97.5 %
-    d  0.522753  0.082263  6.354688  2.088504e-10  0.361521  0.683984
+           coef   std err         t         P>|t|     2.5 %  97.5 %
+    d  0.511722  0.087184  5.869427  4.373034e-09  0.340844  0.6826
 
     Notes
     -----

doubleml/plm/plr.py

@@ -70,7 +70,7 @@ class DoubleMLPLR(LinearScoreMixin, DoubleML):
     >>> dml_plr_obj = dml.DoubleMLPLR(obj_dml_data, ml_g, ml_m)
     >>> dml_plr_obj.fit().summary
            coef   std err          t         P>|t|     2.5 %    97.5 %
-    d  0.482251  0.040629  11.869585  1.703108e-32  0.402619  0.561883
+    d  0.480691  0.040533  11.859129  1.929729e-32  0.401247  0.560135
 
     Notes
     -----

doubleml/rdd/rdd.py

@@ -86,16 +86,20 @@ class RDFlex:
     ...     x=data_dict["X"],
     ...     y=data_dict["Y"],
     ...     d=data_dict["D"],
-    ...     s=data_dict["score"]
+    ...     score=data_dict["score"]
     ... )
     >>> ml_g = RandomForestRegressor()
     >>> ml_m = RandomForestClassifier()
     >>> rdflex_obj = dml.rdd.RDFlex(obj_dml_data, ml_g, ml_m, fuzzy=True)
     >>> print(rdflex_obj.fit())
     Method             Coef.     S.E.     t-stat       P>|t|           95% CI
     -------------------------------------------------------------------------
-    Conventional      0.935     0.220     4.244    2.196e-05  [0.503, 1.367]
-    Robust                 -        -     3.635    2.785e-04  [0.418, 1.396]
+    Conventional      0.950     0.225     4.230    2.333e-05  [0.510, 1.391]
+    Robust                 -        -     3.653    2.589e-04  [0.431, 1.429]
+    Design Type:        Fuzzy
+    Cutoff:             0
+    First Stage Kernel: triangular
+    Final Bandwidth:    [0.74746872]
 
     """
 

pyproject.toml

@@ -49,6 +49,22 @@ dev = [
     "pre-commit>=4.2.0",
 ]
 
+[project.urls]
+Documentation = "https://docs.doubleml.org"
+Source = "https://github.com/DoubleML/doubleml-for-py"
+"Bug Tracker" = "https://github.com/DoubleML/doubleml-for-py/issues"
+
+[tool.pytest.ini_options]
+addopts = [
+    "--doctest-modules",
+    "--doctest-ignore-import-errors"
+]
+doctest_optionflags = [
+    "NORMALIZE_WHITESPACE",
+    "IGNORE_EXCEPTION_DETAIL",
+    "ELLIPSIS"
+]
+
 [tool.black]
 line-length = 127
 target-version = ['py39', 'py310', 'py311', 'py312']
@@ -79,8 +95,3 @@ ignore = [
     # isinstance checks
     "E721",
 ]
-
-[project.urls]
-Documentation = "https://docs.doubleml.org"
-Source = "https://github.com/DoubleML/doubleml-for-py"
-"Bug Tracker" = "https://github.com/DoubleML/doubleml-for-py/issues"